Quantification of some intrinsic soil properties using proximal sensing in arid lands: Application of Vis-NIR, MIR, and pXRF spectroscopy

• Published in: Geoderma Regional Vol. 28; p. e00484
• Main Authors: Naimi, Salman, Ayoubi, Shamsollah, Di Raimo, Luis Augusto Di Loreto, Dematte, Jose Alexandre Melo
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.03.2022
• Subjects: calcium carbonate; clay; electrical conductivity; fluorescence; gypsum; infrared spectroscopy; Iran; least squares; Mid-IR and pXRF spectroscopy; PLSR, arid region; prediction; salinity; sand; silt; soil organic carbon; soil salinity; Soil spectral behavior; soil texture; Vis-NIR-SWIR; X-radiation; Iran; Vis-NIR-SWIR; Soil spectral behavior; Mid-IR and pXRF spectroscopy; PLSR, arid region
• DOI: 10.1016/j.geodrs.2022.e00484
• ISSN: 2352-0094

The aim of the current research was to examine the effectiveness of Vis-NIR-SWIR (visible, near-infrared, and shortwave infrared spectroscopy: 350–2500 nm), MIR (mid-infrared spectroscopy: 4000–400 cm−1), and pXRF (portable x-ray fluorescence) to characterize and estimate clay, sand, silt, calcium carbonate (CaCO3), gypsum (CaSO4.2H2O), soil organic carbon (SOC) contents and electrical conductivity (EC) in Afzar district, Fars province, southern Iran. The study area was divided into non-saline (A) and saline (B) regions, and then a total of 300 soil samples from these areas were collected for laboratory analysis. The partial least-squares regression (PLSR) method was used to predict soil properties from Vis-NIR-SWIR, mid-IR, and pXRF spectra. In general, Vis-NIR-SWIR showed better results for predicting soil texture (R2 for area A: clay = 0.78, sand = 0.80 and silt = 0.69; R2 for area B: clay = 0.63, sand = 0.67 and silt = 0.47) in the arid regions than mid-IR (R2 for area A: clay = 0.71, sand = 0.75 and silt = 0.63; R2 for area B: clay = 0.61, sand = 0.57 and silt = 0.37), and pXRF (R2 for area A: clay = 0.49, sand = 0.60 and silt = 0.50; R2 for area B: clay = 0.41, sand = 0.41 and silt = 0.23). Based on the RPI index, three approaches alone could successfully predict CaCO3 in areas A and B (R2 > 0.75 and RPIQ >2.02). The model prediction results for gypsum showed that although the value of the determination coefficient is high (R2 > 0.95), but according to the low value of the RPIQ index (RPIQ<1.26), the model prediction performance is untrusted, and this is probably due to unsuitable distribution of gypsum in this area. The results showed that the pXRF technique alone could not predict SOC in these areas (R2 < 0.27 and RPIQ <1.08), and it is probably better to use it in combination with Vis-NIR-SWIR and mid-IR data. pXRF for predicting soil salinity in areas A and B was acceptable and successful, respectively. Also, the results showed that the Vis-NIR-SWIR and mid-IR ranges could not predict salinity in these regions and should be used in combination with pXRF data. In conclusion, the Vis-NIR-SWIR, mid-IR, and pXRF spectroscopy are presented here as useful and reliable tools for direct applications in pedology, particularly in the digital mapping of soil surface properties. •The effectiveness of Vis-NIR-SWIR, and MIR were examined to estimate some soil properties.•Vis-NIR-SWIR showed better results for soil texture than mid-IR and PXRF.•Each technique alone could successfully predict CaCO3 in the study area•The results showed that the PXRF technique alone is not able to predict SOC in these areas.•Vis-NIR-SWIR, mid-IR and PXRF reflectance spectroscopy are presented here as useful and reliable tools